Database calculation engine

ABSTRACT

A select query or a data referencing a calculation scenario is received by a database server from a remote application server. The specified calculation scenario is one of a plurality of calculation scenarios and it defines a data flow model that includes one or more calculation nodes. Each calculation node defines one or more operations to execute on the database server. Thereafter, the database server instantiates the specified calculation scenario and executes the operations defined by the calculation nodes of the instantiated calculation scenario to result in a responsive data set. This data set is then provided by the database server to the application server. Related apparatus, systems, techniques and articles are also described.

CROSS REFERENCE TO RELATED APPLICATION

This application is a Continuation of patent application Ser. No.12/914,445, filed on Oct. 28, 2010, entitled “Database CalculationEngine”, the contents of which are hereby fully incorporated byreference.

TECHNICAL FIELD

The subject matter described herein relates to a database calculationengine and the use of calculation scenarios to execute database queries.

BACKGROUND

Data flow between an application server and a database server is largelydependent on the scope and number of queries generated by theapplication server. Complex calculations can involve numerous queries ofthe database server which in turn can consume significant resources inconnection with data transport as well as application server-sideprocessing of transported data.

SUMMARY

In one aspect, a calculation scenario or a reference to a calculationscenario is received by a database server from a remote applicationserver. The specified calculation scenario is one of a plurality ofcalculation scenarios and it defines a data flow model that includes oneor more calculation nodes. Each calculation node defines one or moreoperations to execute on the database server. Thereafter, the databaseserver instantiates the specified calculation scenario and executes theoperations defined by the calculation nodes of the instantiatedcalculation scenario to result in a responsive data set. This data setis then provided by the database server to the application server.

In some implementations, at least a portion of paths and/or attributesdefined by the calculation scenario are not required to respond to thequery. In such cases, the instantiated calculation scenario omits thepaths and attributes defined by the calculation scenario that are notrequired to respond to the query. In other cases, the entire calculationscenario is utilized and no paths or attributes are removed.

At least one of the calculation nodes can filter and/or sort resultsobtained from the database server prior to transmission of the resultset. The calculation scenario can be instantiated in a calculationengine layer by a calculation engine. The calculation engine layer caninteract with a physical table pool and a logical layer. The physicaltable pool can comprise physical tables containing data to be queriedand the logical layer can define a logical metamodel joining at least aportion of the physical tables in the physical table pool. An input foreach calculation node can comprise one or more of: a physical index, alogical index (e.g., join index, OLAP index, etc.), and anothercalculation node. Each calculation node can have at least one outputtable that is used to generate the final result set. At least onecalculation node can consume an output table of another calculationnode.

The executing can comprise forwarding the query to a calculation node inthe calculation scenario that is identified as a default node if thequery does not specify a calculation node at which the query should beexecuted. In other cases, the query identifies a particular calculationnode and the query is forwarded to such node for execution.

The calculation scenario can comprises database metadata. Thecalculation scenario can be exposed as a database calculation view. Theexecuting can comprise invoking, by a SQL processor, a calculationengine to execute the calculation scenario behind the databasecalculation view. The calculation engine can invoke the SQL processorfor executing set operations and SQL nodes and on the other hand the SQLprocessor invokes the calculation engine when executing SQL queries withcalculation views.

Articles of manufacture are also described that comprise computerexecutable instructions permanently stored on computer readable media(e.g., non-transitory media, etc.), which, when executed by a computer,causes the computer to perform operations herein. Similarly, computersystems are also described that may include at least one processor and amemory coupled to the processor. The memory may temporarily orpermanently store one or more programs that cause the processor toperform one or more of the operations described herein. For example, asystem can include a database server and an application server (witheach having at least one corresponding computer system including atleast one processor and memory).

The subject matter described herein provides many advantages. Forexample, in a combined environment with a database server and anapplication server, at execution time of a query, there can be severalroundtrips of data between such servers. The current subject matter, byproviding intermediate results at the database server, greatly reducesthe amount of data transported to the application server.

The details of one or more variations of the subject matter describedherein are set forth in the accompanying drawings and the descriptionbelow. Other features and advantages of the subject matter describedherein will be apparent from the description and drawings, and from theclaims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a process flow diagram illustrating a method of querying adatabase using a calculation scenario; and

FIG. 2 is a diagram illustrating a calculation engine layer, a logicallayer, a physical table pool and their interrelationship;

FIG. 3 is a diagram illustrating a first instantiation of a calculationscenario;

FIG. 4 is a diagram illustrating a second instantiation of a calculationscenario;

FIG. 5 is a diagram illustrating an architecture for processing andexecution control;

FIG. 6 is a diagram of a graphical user interface illustrating a dunningcalculation scenario; and

FIG. 7 is a diagram showing a portion of the dunning calculationscenario of FIG. 6.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

FIG. 1 is a process flow diagram 100 illustrating a method in which, at110, a calculation scenario or a reference to a calculation scenario(e.g., a select query referencing the calculation scenario, etc.) isreceived by a database server from a remote application server. Thespecified calculation scenario is one of a plurality of calculationscenarios and it defines a data flow model that includes one or morecalculation nodes. Each calculation node defines one or more operationsto execute on the database server. Thereafter, at 120, the databaseserver instantiates the specified calculation scenario and, at 130,executes the operations defined by the calculation nodes of theinstantiated calculation scenario to result in a responsive data set.This data set is then provided, at 140, by the database server to theapplication server.

The subject matter described herein can enable an application developerto define a data flow model to push down a high level algorithm to adatabase. A developer can define a calculation scenario which describesthis algorithm in a general way as data flow consisting of calculationnodes. A calculation node as used herein represents a operation such asa projection, aggregation, join, union, minus, intersection, and thelike. Additionally, as described below, in addition to a specifiedoperation, calculation nodes can sometimes be enhanced by filteringand/or sorting criteria. In some implementations, calculated attributescan also be added to calculation nodes.

During query time (i.e., the time in which a database is queried), thedata flow specified by a calculation scenario is instantiated. Duringinstantiation, the calculation scenario is compacted to only includequeries requirements by removing useless pathes and attributes (that arenot requested) within the calculation scenario. This compaction reducescalculation time and also minimizes the total amount of data that mustbe processed.

FIG. 2 is a diagram 200 that illustrates a database system in whichthere are three layers, a calculation engine layer 210, a logical layer220, and a physical table-pool 230. Calculation scenarios can beexecuted by a calculation engine which can form part of a database orwhich can be part of the calculation engine layer 210 (which isassociated with the database). The calculation engine layer 210 can bebased on and/or interact with the other two layers, the logical layer220 and the physical table pool 230. The basis of the physical tablepool 230 consists of physical tables (called indexes) containing thedata. Various tables can then be joined using logical metamodels definedby the logical layer 220 to form a new index. For example, the tables ina cube (OLAP index) can be assigned roles (e.g., fact or dimensiontables) and joined to form a star schema. It is also possible to formjoin indexes, which can act like database view in environments such asthe Fast Search Infrastructure (FSI) by SAP AG.

As stated above, calculation scenarios can include individualcalculation nodes 211-214, which in turn each define operations such asjoining various physical or logical indexes and other calculation nodes(e.g., CView 4 is a join of CView 2 and CView 3). That is, the input fora calculation node 211-214 can be one or more physical, join, or OLAPindexes or calculation nodes.

In calculation scenarios, two different representations can be provided.First, a pure calculation scenario in which all possible attributes aregiven. Second, an instantiated model that contains only the attributesrequested in the query (and required for further calculations). Thus,calculation scenarios can be created that can be used for variousqueries. With such an arrangement, calculation scenarios can be createdwhich can be reused by multiple queries even if such queries do notrequire every attribute specified by the calculation scenario.

Every calculation scenario can be uniquely identifiable by a name (i.e.,the calculation scenario can be a database object with a uniqueidentifier, etc.). This means, that the calculation scenario can bequeried in a manner similar to a view in a SQL database. Thus, the queryis forwarded to the calculation node 211-214 for the calculationscenario that is marked as the corresponding default node. In addition,a query can be executed on a particular calculation node 211-214 (asspecified in the query). Furthermore, nested calculation scenarios canbe generated in which one calculation scenario is used as source inanother calculation scenario (via a calculation node 211-214 in thiscalculation scenario). Each calculation node 211-214 can have one ormore output tables. One output table can be consumed by severalcalculation nodes 211-214.

FIG. 3 is a diagram 300 illustrating an example of a calculationscenario that relates a number of sales to total sales. Withconventional arrangements, such a query can be expressed with severalSQL statements but not in a single statement, because for thecalculation of the relation two aggregations at different aggregationlevels are required. To calculate the number of sales, aggregation isperformed by a requested GroupBy attribute. To calculate the salestotal, all sales need to be aggregated. Previously this required twoseparate requests on different SQL view, and the final calculation hadto be performed in the application (as opposed to database-side).

For this example, that data source is an OLAP cube “SalesCube” 330,which has the three dimensions Customer, Year, and Product as well asthe measure Sales. As stated, this data source 310 can be entered as aspecial DataSource node in the logical layer 220 in the calculationscenario. The DataSource is now referenced from two calculation nodes.The calculation node TotalsCV 320 on the left side calculates the salestotal, by simply summing the sales without any GroupBy attributes. Thecalculation node SalesCV 330 on the right side calculates the salesaccording to the GroupBys. To calculate their relationship, the twocalculation nodes 320, 330 are joined with each other using a CrossJoin.In the calculation node RatioCV 340 after the join, all the attributesneeded for the calculation are available and a new calculated attributeRatio is provided.

The implementation of FIG. 3 is a general calculation scenario. That is,if the calculation scenario is queried via a SQL statement which onlyrequests product as GroupBy attribute, the model is appropriatelyinstantiated and executed. FIG. 4 is a diagram 400 illustrating avariation in which not all of the attributes specified by thecalculation nodes 330, 340 are required. In particular, the ratiocalculation is for sales relative to total sales without regard tocustomer and year. In the instantiation, the unnecessary attributesCustomer and Year are removed from the calculation nodes RatioCv 340 andSalesCV 330 which accelerates execution of the results (e.g., the ratio)because less data has to be touched (i.e., fewer attributes need to besearched/persisted, etc.).

FIG. 5 is a diagram 500 illustrating a sample architecture for requestprocessing and execution control. As shown in FIG. 5, artifacts 505 indifferent domain specific languages can be translated by their specificcompilers 510 into a common representation called a “calculationscenario” 515 (illustrated as a calculation model). To achieve enhancedperformance, the models and programs written in these languages areexecuted inside the database server. This arrangement eliminates theneed to transfer large amounts of data between the database server andthe client application. Once the different artifacts 505 are compiledinto this calculation scenario 515, they can be processed and executedin the same manner. The execution of the calculation scenarios 515(i.e., calculation scenarios) is the task of a calculation engine 520.

The calculation scenario 515 can be a directed acyclic graph with arrowsrepresenting data flows and nodes that represent operations. Eachcalculation node has a set of inputs and outputs and an operation thattransforms the inputs into the outputs. In addition to their primaryoperation, each calculation node can also have a filter condition forfiltering the result set. The inputs and the outputs of the operationscan be table valued parameters (i.e., user-defined table types that arepassed into a procedure or function and provide an efficient way to passmultiple rows of data to the application server). Inputs can beconnected to tables or to the outputs of other calculation nodes.Calculation scenarios 515 can support a variety of node types such as(i) nodes for set operations such as projection, aggregation, join,union, minus, intersection, and (ii) SQL nodes that execute a SQLstatement which is an attribute of the node. In addition, to enableparallel execution, a calculation scenario 515 can contain split andmerge operations. A split operation can be used to partition inputtables for subsequent processing steps based on partitioning criteria.Operations between the split and merge operation can then be executed inparallel for the different partitions. Parallel execution can also beperformed without split and merge operation such that all nodes on onelevel can be executed in parallel until the next synchronization point.Split and merge allows for enhanced/automatically generatedparallelization. If a user knows that the operations between the splitand merge can work on portioned data without changing the result he orshe can use a split. Then, the nodes can be automatically multipliedbetween split and merge and partition the data.

A calculation scenario 515 can be defined as part of database metadataand invoked multiple times. A calculation scenario 515 can be created,for example, by a SQL statement “ALTER SYSTEM ADD SCENARIO <xml OR jsonrepresenting the scenario>”. Once a calculation scenario 515 is created,it can be queried (e.g., “SELECT A, B, C FROM <scenario name>”, etc.).In some cases, databases can have pre-defined calculation scenarios 515(default, previously defined by users, etc.). The calculation scenarios515 can be persisted in a repository (coupled to the database server) orin transient scenarios, the calculation scenarios 515 can be keptin-memory.

Calculation scenarios 515 are more powerful than traditional SQL queriesor SQL views for many reasons. One reason is the possibility to defineparameterized calculation schemas that are specialized when the actualquery is issued. Unlike a SQL view, a calculation scenario 515 does notdescribe the actual query to be executed. Rather, it describes thestructure of the calculation. Further information is supplied when thecalculation scenario is executed. This further information can includeparameters that represent values (for example in filter conditions). Toobtain more flexibility, it is also possible to refine the operationswhen the model is invoked. For example, at definition time, thecalculation scenario 515 may contain an aggregation node containing allattributes. Later, the attributes for grouping can be supplied with thequery. This allows having a predefined generic aggregation, with theactual aggregation dimensions supplied at invocation time. Thecalculation engine 520 can use the actual parameters, attribute list,grouping attributes, and the like supplied with the invocation toinstantiate a query specific calculation scenario 515. This instantiatedcalculation scenario 515 is optimized for the actual query and does notcontain attributes, nodes or data flows that are not needed for thespecific invocation.

When the calculation engine 520 gets a request to execute a calculationscenario 515, it can first optimize the calculation scenario 515 using arule based model optimizer 522. Examples for optimizations performed bythe model optimizer can include “pushing down” filters and projectionsso that intermediate results 526 are narrowed down earlier, or thecombination of multiple aggregation and join operations into one node.The optimized model can then be executed by a calculation engine modelexecutor 524 (a similar or the same model executor can be used by thedatabase directly in some cases). This includes decisions about parallelexecution of operations in the calculation scenario 515. The modelexecutor 524 can invoke the required operators (using, for example, acalculation engine operators module 528) and manage intermediateresults. Most of the operators are executed directly in the calculationengine 520 (e.g., creating the union of several intermediate results).The remaining nodes of the calculation scenario 515 (not implemented inthe calculation engine 520) can be transformed by the model executor 524into a set of logical database execution plans. Multiple set operationnodes can be combined into one logical database execution plan ifpossible.

The calculation scenarios 515 of the calculation engine 520 can beexposed as a special type of database views called calculation views.That means a calculation view can be used in SQL queries and calculationviews can be combined with tables and standard views using joins and subqueries. When such a query is executed, the database executor inside theSQL processor needs to invoke the calculation engine 520 to execute thecalculation scenario 515 behind the calculation view. In someimplementations, the calculation engine 520 and the SQL processor arecalling each other: on one hand the calculation engine 520 invokes theSQL processor for executing set operations and SQL nodes and, on theother hand, the SQL processor invokes the calculation engine 520 whenexecuting SQL queries with calculation views.

FIG. 6 is a diagram of a modeling tool 600 rendering a complexcalculation scenario relating to a dunning run (which is the process ofcommunicating with customers to ensure the collection of accountsreceivable). A company triggers the dunning run to identify thecustomers who have outstanding accounts. To identify these customers acomplex application logical must be execute:

-   -   Currency: Not all bills are paid in the same currency, so each        item must be checked if currency conversion is required and the        currency conversion must be applied to transform the item into        the local currency of the company.    -   Balances between both companies need to be compared in order to        determine whether there is a surplus or deficit.    -   The currency conversion must be performed on an item level and        afterward the aggregation value must be displayed. As a result,        during the run, a lot of data in tables has to be touched and        the intermediate results can be quite large

The modeling tool 600 illustrates all calculation nodes 610 of thedunning run which are illustrated in a hierarchical arrangement. Textversions of these calculation nodes 610 are displayed as elements 630 ina window 620 within the modeling tool 600. Each of the calculation nodes610 represents a database operation (join, projection, aggregation,union, etc.). Selection of the graphical user interface elements 630 cancause, for example, information relating to the corresponding databaseoperation to be displayed. All together, the calculation nodes 610define a data flow starting at bottom at the calculation nodes 610-A andgoing up to a root node 610-B at the top.

If a single SQL statement for all of these operations were to becreated, it would be difficult to understand (from a human perspective)which would make it more prone for human error. In contrast, with thecurrent calculation scenario, one can still see the structure of thedata flow model and access each node of the model to display theresults.

FIG. 7 is a diagram 700 illustrating a portion of the calculation nodes610 illustrated in FIG. 6. In particular, this diagram 700 shows asection of the currency conversion in the Dunning Run. As illustrated,one node can be used by two or three other nodes and such nodes executeadditional filters to select the required data from the incoming node.These filters handle a typical requirement in the currency conversionprocess because often a value in a special column defines what type ofconversion must be applied to a particular row and then a totallydifferent data flow must be applied. Such operations/filters in SQLwould require multiplying the same sub SQL statement for each selection.This requirement can be difficult, if not impossible, to implement.

FIGS. 6 and 7 demonstrate that if a whole dunning scenario is expressedin one SQL statement, the currency conversion would only be useablewithin that particular scenario. In contrast, with the current subjectmatter, logic within calculation scenarios can be re-used (which allowfor an easier creation of complementary calculation scenarios). Forexample, with regard to dunning, a report can be generated that liststhe suppliers for that the own company has open bills. In such acalculation scenario, currency conversion is also required so thecorresponding logic can be added to the calculation scenario such thatthe calculation nodes 610 that are required for the currency conversioncan be re-used.

Various implementations of the subject matter described herein may berealized in digital electronic circuitry, integrated circuitry,specially designed ASICs (application specific integrated circuits),computer hardware, firmware, software, and/or combinations thereof.These various implementations may include implementation in one or morecomputer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichmay be special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and at least one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor, and may be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the term “machine-readable medium” refers toany computer program product, apparatus and/or device (e.g., magneticdiscs, optical disks, memory, Programmable Logic Devices (PLDs)) used toprovide machine instructions and/or data to a programmable processor,including a machine-readable medium that receives machine instructionsas a machine-readable signal. The term “machine-readable signal” refersto any signal used to provide machine instructions and/or data to aprogrammable processor.

To provide for interaction with a user, the subject matter describedherein may be implemented on a computer having a display device (e.g., aCRT (cathode ray tube) or LCD (liquid crystal display) monitor) fordisplaying information to the user and a keyboard and a pointing device(e.g., a mouse or a trackball) by which the user may provide input tothe computer. Other kinds of devices may be used to provide forinteraction with a user as well; for example, feedback provided to theuser may be any form of sensory feedback (e.g., visual feedback,auditory feedback, or tactile feedback); and input from the user may bereceived in any form, including acoustic, speech, or tactile input.

The subject matter described herein may be implemented in a computingsystem that includes a back-end component (e.g., as a data server), orthat includes a middleware component (e.g., an application server), orthat includes a front-end component (e.g., a client computer having agraphical user interface or a Web browser through which a user mayinteract with an implementation of the subject matter described herein),or any combination of such back-end, middleware, or front-endcomponents. The components of the system may be interconnected by anyform or medium of digital data communication (e.g., a communicationnetwork). Examples of communication networks include a local areanetwork (“LAN”), a wide area network (“WAN”), and the Internet.

The computing system may include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

Although a few variations have been described in detail above, othermodifications are possible. For example, the logic flow depicted in theaccompanying figures and described herein do not require the particularorder shown, or sequential order, to achieve desirable results. Otherembodiments may be within the scope of the following claims.

What is claimed is:
 1. A method comprising: receiving, by a databaseserver from a remote application server, a select query specifying acalculation scenario, the specified calculation scenario being one of aplurality of calculation scenarios and defining a data flow model thatincludes one or more calculation nodes, each calculation node definingone or more operations to execute on the database server to respond tothe query, at least one calculated node defining a calculated attribute;instantiating, by the database server, the specified calculationscenario, wherein at least a portion of paths and/or attributes definedby the specified calculation scenario are not required to respond to thequery, and instantiating the specified calculation scenario includesomitting the paths and attributes defined by the specified calculationscenario that are not required to respond to the query; executing, bythe database server, the operations defined by the calculation nodes ofthe instantiated calculation scenario to result in data set responsiveto the query; and providing, by the database server to the applicationserver, the data set.
 2. A method as in claim 1, wherein at least one ofthe calculation nodes filters results obtained from the database server.3. A method as in claim 1, wherein at least one of the calculation nodessorts results obtained from the database server.
 4. A method as in claim1, wherein the calculation scenario is instantiated in a calculationengine layer by a calculation engine.
 5. A method as in claim 4, whereinthe calculation engine layer interacts with a physical table pool and alogical layer, the physical table pool comprising physical tablescontaining data to be queried, and the logical layer defining a logicalmetamodel joining at least a portion of the physical tables in thephysical table pool.
 6. A method as in claim 1, wherein an input foreach calculation node comprises one or more of: a physical index, a joinindex, an OLAP index, and another calculation node.
 7. A method as inclaim 6, wherein each calculation node has at least one output tablethat is used to generate the final result set.
 8. A method as in claim7, wherein at least one calculation node consumes an output table ofanother calculation node.
 9. A method as in claim 1, wherein theexecuting comprises: forwarding the query to a calculation node in thecalculation scenario that is identified as a default node if the querydoes not specify a calculation node at which the query should beexecuted.
 10. A method as in claim 1, wherein the query identifies aparticular calculation node, and wherein the executing comprises:forwarding the query to the calculation node specified in the query atwhich the query should be executed.
 11. A method as in claim 1, whereinthe calculation scenario comprises database metadata.
 12. A method as inclaim 1, wherein the calculation scenario is exposed as a databasecalculation view.
 13. A method as in claim 12, wherein the executingcomprises: invoking, by a SQL processor, a calculation engine to executethe calculation scenario behind the database calculation view.
 14. Amethod as in claim 13, wherein the calculation engine invokes the SQLprocessor for executing set operations.
 15. A method as in claim 14,wherein the SQL processor invokes the calculation engine when executingSQL queries with calculation views.
 16. A system comprising: a databaseserver comprising memory and at least one data processor; and anapplication server in communication with and remote from the databaseserver comprising memory and at least one data processor; wherein: thedatabase server receives, from the application server, a queryassociated with a calculation scenario, the calculation scenario beingone of a plurality of calculation scenarios and defining a data flowmodel that includes one or more calculation nodes, each calculation nodedefining one or more operations to execute on the database server torespond to the query, at least one calculated node defining a calculatedattribute; the database server instantiates the calculation scenario,wherein at least a portion of paths and/or attributes defined by thecalculation scenario are not required to respond to the query, andinstantiating the calculation scenario includes omitting the paths andattributes defined by the calculation scenario that are not required torespond to the query; the database server executes the operationsdefined by the calculation nodes of the instantiated calculationscenario to result in a data set responsive to the query; the databaseserver provides the data set to the application server; and thecalculation scenario being reusable, in whole or in part, acrossmultiple different queries.
 17. A system as in claim 16, wherein thereare a plurality of application servers coupled to the database server.18. A system as in claim 16, wherein the database server executes threelayers, a calculation engine layer, a logical layer, and a physicaltable pool.
 19. A non-transitory computer program product storinginstructions, which when executed by at least one data processor formingpart of at least one computing system, result in operations comprising:receiving data generated by an application server that comprises a querythat specifies a calculation scenario, the specified calculationscenario being one of a plurality of calculation scenarios and defininga data flow model that includes one or more calculation nodes, eachcalculation node defining one or more operations to execute on thedatabase server to respond to the query, at least one calculated nodedefining a calculated attribute; instantiating the specified calculationscenario in a calculating engine layer, the calculation engine layerinteracting with a physical table pool and a logical layer, the physicaltable pool comprising physical tables containing data to be queried, andthe logical layer defining a logical metamodel joining at least aportion of the physical tables in the physical table pool, wherein atleast a portion of paths and/or attributes defined by the specifiedcalculation scenario are not required to respond to the query, andinstantiating the specified calculation scenario includes omitting thepaths and attributes defined by the specified calculation scenario thatare not required to respond to the query; executing the operationsdefined by the calculation nodes of the instantiated calculationscenario to result in a data set responsive to the query; and providingthe data set to the application server.